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Border irregularity loss for automated segmentation of primary brain lymphomas on post-contrast MRI

  • El Jurdi, Rosana
  • Nichelli, Lucia
  • Alentorn, Agusti
  • Vaillant, Ghislain
  • Fu, Guanghui
  • Hoang-Xuan, Khê
  • Houillier, Caroline
  • Lehéricy, Stéphane
  • Colliot, Olivier
Publication Date
Feb 18, 2024
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Unlike for other brain tumors, there has been little work on the automatic segmentation of primary central nervous system (CNS) lymphomas. This is a challenging task due the highly variable pattern of the tumor and its boundaries. In this work, we propose a new loss function that controls border irregularity for deep learning-based automatic segmentation of primary CNS lymphomas. We introduce a border irregularity loss which is based on the comparison of the segmentation and it smoothed version. The border irregularity loss is combined with a previously proposed topological loss to better control the different connected components. The approach is general and can be used with any segmentation network. We studied a population of 99 patients with primary CNS lymphoma. 40 patients were isolated from the very beginning and formed the independent test set. The segmentations were performed on post-contrast T1-weighted MRI. The MRI were acquired in clinical routine and were highly heterogeneous. The proposed approach substantially outperformed the baseline across the various evaluation metrics (by 6 percent points of Dice, 40mm of Hausdorff distance and 6mm of mean average surface distance). However, the overall performance was moderate, highlighting that automatic segmentation of primary CNS lymphomas is a difficult task, especially when dealing with clinical routine MRI. The code is publicly available here:

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